How American Express uses Big Data to transform operations

In this article, we look at the final company featured in February’s Big Data feature in Supply Chain Digital — American Express. 

Big Data is at the heart of American Express’ decision-making. Its effect is prevalent in two key areas: detecting fraud and bringing merchants and customers closer together. Currently, there are over 110 million AmEx cards in operation, as well as more than US$1trn in transactions processed. In total, American Express handles over 25% of US credit card activity. The primary aim of AmEx is to detect fraudulent transactions efficiently in order to implement a machine learning model that leverages a range of data sources. This includes things such as card membership information, spending details and merchant information to track fraudulent activity and make a decision in milliseconds.

SEE ALSO:

• How is Big Data transforming the supply chain?

• Starbucks: driving Big Data to boost business

• How Amazon uses Big Data to transform operations

• Read the latest issue of Supply Chain Digital here!

Ensuring customer’s finances are protected is essential to banks, which means that the importance of monitoring against cyberattacks is paramount. For consumers, American Express is utilising vast data flows to develop apps that can connect a cardholder with products or services. The apps offer benefits to cardholders that use the app, as well as providing an incentive for more businesses to accept American Express.

In 2010, the company upgraded from traditional database technology to a Hadoop infrastructure and brought in machine learning algorithms. In a bid to centrally focus on Big Data, American Express has opened a tech lab in Palo Alto, California.

To read more about how American Express and other companies are using Big Data, click here!

For more information on all topics for Procurement, Supply Chain & Logistics - please take a look at the latest edition of Supply Chain Digital magazine.

Follow us on LinkedIn and Twitter.